Research on Expansion Neighborhood and Cost Function-Based A* Algorithm

The ENCF-A
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(Expansion Neighborhood and Cost Function-Based A
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) algorithm tackles challenges in the traditional A
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method such as lengthy paths and numerous turns. It achieves this by expanding the search scope, elongating step distances, and minimizing path deflection angles. The algorithm's efficiency is enhanced through the incorporation of obstacle distribution and a cost function that encourages movement toward the target. Additionally, the introduction of a turn cost function and the removal of redundant nodes prevent unnecessary detours, resulting in more realistic motion paths. Experimental outcomes exhibit significant improvements: ENCF-A
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reduces path nodes by 82.61%, decreases deflection angles by 71.96%, and shortens path distances by 9.71%. Consequently, this algorithm notably enhances the efficiency of tracing Automated Guided Vehicles (AGVs) in intricate environments.